Application-Oriented License Plate Recognition

• Published in: IEEE transactions on vehicular technology 2013-02, Vol.62 (2), p.552-561
• Main Authors: Hsu, Gee-Sern, Chen, Jiun-Chang, Chung, Yu-Zu
• Format: Article
• Language: English
• Subjects: Access methods and protocols, osi model; Applied sciences; Cameras; Categories; Character recognition; Character segmentation; Communities; Exact sciences and technology; Image edge detection; Information, signal and communications theory; License plate recognition; License plates; Licenses; Lighting; Pattern recognition; plate detection; Recognition; Roads; Segmentation; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Studies; Telecommunications; Telecommunications and information theory; Teleprocessing networks. Isdn; vehicle license plate recognition (LPR); Vehicles
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2012.2226218

We split the applications of vehicle license plate recognition (LPR) into three major categories and propose a solution with parameter settings that are adjustable for different applications. The three categories are access control (AC), law enforcement (LE), and road patrol (RP). Each application is characterized by variables of different variation scopes and thus requires different settings on the solution with which to deal. The proposed solution consists of three modules for plate detection, character segmentation, and recognition. Edge clustering is formulated for solving plate detection for the first time. It is also a novel application of the maximally stable extreme region (MSER) detector to character segmentation. A bilayer classifier, which is improved with an additional null class, is experimentally proven to be better than previous methods for character recognition. To assess the performance of the proposed solution, the application-oriented license plate (AOLP) database is composed and made available to the research community. Experiments show that the proposed solution outperforms many previous solutions, and LPR can be better solved by solutions with settings oriented for different applications.